How to Evaluate and Select the Right AutoML Platform 
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  • Sachin Andhare
  • Blog
  • May 7, 2020

How to Evaluate and Select the Right AutoML Platform 

If you are in the market looking for automated machine learning  (AutoML) tools, there are plenty of choices. Forrester Research recently published a report highlighting nine Automation Focussed Machine Learning Solutions and named dotData a leader. The report underscores the importance of Feature Engineering and Explainability as key differentiating factors for leaders in the AutoML space. But if you are new to machine learning or are part of a BI and analytics team with a mandate to incorporate predictive analytics, how do you decide which AutoML tool is right for you? What are some of the factors that you should consider as you make your decision?

The end-user & skill set

Any data science project is going to start with identifying business use cases and requirements. The process is also heavily dependent on the available resources of the business as well as the skill-set of the primary intended users. In order to make the best possible choice, organizations should start their evaluation by asking some fundamental questions:

  1. Who will be the primary intended users of the AutoML platform? The Data Science Team or the BI team?
  2. What are the skill-level and data science expertise of the primary user?
  3. Is the primary programming environment of the intended users Python?

The motivation for using an AutoML platform may be completely different depending on the user persona. If the intended users are data scientists, the primary environment is Python/R, then you need a platform that offers a great amount of customization. Advanced analytical developers and data scientists may want to use an AutoML platform to generate new features but prefer to tweak models manually. On the other hand, BI & analytics team may be struggling with the long lead times to prepare data, need help with algorithm selection and want to use a tool that automates the data science workflow.

The data science workflow

Traditional Data Science Process

How much of this process do you need to automate?

Top factors

Here is a quick rundown of major attributes to think through while evaluating an AutoML platform:

  1. Data Ingestion and Preparation: How much manipulation of data must be performed before it is ready for ingestion by the AutoML platform? Can you upload data to the AutoML platform without having to write additional SQL code? 
  2. Feature Engineering Automation: How much manual work is involved in Feature Engineering? Can the system automatically explore all available database entity relationships and discover and evaluate features based on available columns and relationships? 
  3. Machine Learning: Does the system support state-of-the-art ML algorithms like scikit-learn, XGBoost, LightGBM, TensorFlow and PyTorch? Can the users perform an automated hyper-parameter search of ML algorithms?
  4. Production & Operationalization: How easy is it to deploy ML models in a production environment? Can you monitor models, discover data drift and quickly  retrain models if production data changes over time?
  5. Platform Accessibility, Ease of Use and Deployment Flexibility: Can all steps of the data science process be executed seamlessly within a single platform without the need for moving between systems and applications? 

Last but not the least, is it easy for non-data scientists to understand the workflow of the application, the concepts, and steps necessary to proceed?

To learn more about Automation-Focussed Machine Learning Solutions, the Forrester Wave report is a great resource. For guidance on top factors to consider while selecting an AutoML platform , check out our latest AutoML Evaluation Guide here.

 

Learn more about dotData:

dotData Enterprise

Why dotData

Why AutoML 2.0

 

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  • Carl Bowen
  • Blog
  • August 21, 2019

Automated Machine Learning vs. Data Science Automation [Infographic]

A lot has been written over the past few years about AutoML. Automated Machine Learning is a rapidly growing category of software platforms in the field of data science. Looking at the world of data science strictly from the perspective of automating the machine learning component leaves a lot to be desired. In fact, the vast majority of the work that data scientists must perform is often associated with the tasks that preceded the selection and optimization of ML models.

The automation of feature engineering is at the heart of data science. The infographic below shows a side-by-side comparison of how typical “AutoML” platforms can help the data scientist vs. data science automation:

Made with Visme Infographic Maker

OR Linked at:

Infographic: data science automation vs automl

 

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  • Carl Bowen
  • Press Releases EN
  • August 13, 2019

dotData CEO to Present on Innovations in Data Science Automation at Ai4 Finance 2019

SAN MATEO, Calif., August 13, 2019 – dotData, the first and only company focused on delivering full-cycle data science automation and operationalization for the enterprise, today announced that its CEO, Ryohei Fujimaki, Ph.D., will present on the latest innovations in data science automation at Ai4 Finance 2019. The session will take place at 11:45 a.m. on Thursday, August 22nd, at Convene, 117 W 46th Street, New York City. 
At the conference, Dr. Fujimaki will provide a technical overview of data science automation, focusing on the key innovations that are driving the world of Automated Machine Learning. His presentation will offer insights for technical and business users in the financial services community who need to create a roadmap for their data science practice.
The Ai4 Finance conference brings together business leaders and data practitioners to facilitate the adoption of artificial intelligence and machine learning technologies by delivering actionable insights from those working on the frontlines of AI in the enterprise. 
The dotData Platform accelerates the entire data science process from months to days, enabling companies to rapidly scale their AI/ML initiatives to drive transformative business changes. The dotData Platform also democratizes the data science process by enabling more participants with different skill levels to effectively execute on projects, making it possible for enterprises to operationalize 10x more projects with transparent and actionable outcomes.
“To deliver services and products across multiple channels and with universal access, banks need to be able to leverage their available data to predict how client needs are evolving, what products and services are most likely to be beneficial and their preferred method of interaction,” said Dr. Fujimaki. “Using dotData, banks can leverage the power of AI and Machine Learning, powered by data science acceleration, to optimize their client portfolio offerings.”
If you are interested in meeting with dotData at Ai4 Finance 2019, email: jmoritz@0to5.com.  For more information or a demo of dotData Enterprise or dotDataPy, visit dotData.com.

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  • Carl Bowen
  • Blog
  • July 18, 2019

How Will Automation Change Enterprise Data Science? – Part 2

continued from last week’s post…

dotData, Data Science Without The Headaches

dotData is a brand new breed of AutoML product that provides what we call Full Cycle Data Science Automation. At the heart of our vision is the idea that the data science process should be fast, easy to perform, and easy to analyze and deploy, from raw business data to the business values. Our vision has led us to develop dotData Enterprise and dotData Py, two related platforms that leverage the same automation engine in uniquely different ways. dotData Enterprise is ideal for the citizen data scientist: fully automated, point-and-click driven, and ready to automate 100% of the data science process without requiring in-depth knowledge of how data science works. dotData Py, on the other hand, is ideal for data scientists. dotData Py provides a python library for Jupyter notebooks, one of the most popular data science platforms available.

dotData Enterprise

dotData Enterprise

 

With dotData Enterprise, citizen-data scientists can work on data science projects without having to learn how to become full-fledged data scientists.

dotDataPy

dotDataPy

 

With dotData Py, data scientists can leverage the benefits of automated feature engineering to dramatically shorten development times, while still retaining the high degree of control and customization that their job requires.

 

Four Pillars to Change Data Science

Accelerate

dotData helps enterprise organizations accelerate their adoption and monetization of Artificial Intelligence (AI) and machine learning (ML) projects. Our full-cycle data science automation accelerates every step of the process, including the data wrangling and feature engineering that often takes months to complete. With dotData, the data science team can execute 10x more projects. Data science is eventually test-and-learn, and the significantly-short turnaround allows you to find critical use cases faster.

Democratize

dotData’s platform is designed to take the hard part of the data science process and automate it. With dotData, a more comprehensive range of people like BI engineers or business analysts can execute and contribute to data science projects, which genuinely democratizes and scale-out data science in the organization. Further, by leveraging “citizen” data scientists for common use cases, data scientists can focus on higher-impact and more challenging tasks.

Augment 

dotData’s AI-powered feature engineering can explore millions of feature hypotheses for a given use case. The automation augments the ability of data scientists and even domain experts to test many more hypotheses than ever before and delivers new business insights through transparent features. 

Operationalize

dotData automatically produces production-ready feature-generating pipeline and ML scoring models and operationalizes them through dotData APIs. The implementation is as simple as adding one line of code, and even more importantly, the maintenance of the entire production workflows, i.e., retraining features and ML models, is also automated. 

AutoML Results That Speak For Themselves

How does the whole process work in real-life environments? dotData has been able to accelerate the AI and machine learning production of global companies like Japan Airlines and SMBC Financial Services and has reduced development efforts from months to days. In fact, in a recent test with a global, fortune 50 consumer electronics company, dotData was able to replicate AI projects that had taken five months each to complete in less than three days. dotData clients see a return on their investment in a matter of days and can finally begin to reduce the high failure rates that have plagued AI and ML shops, and that continues to limit the promise of AI. To learn more about dotData and our products, contact our sales team at contact@dotdata.com, or visit our website at dotdata.com.

The Right Tools For The Right People

A final step in accelerating data science is to create AutoML solutions that provide the right working environment for the right individual. The notion of empowering “citizen data scientists” is not new. The idea that, with automation, non-data scientist trained users like BI analysts can contribute to the AI and machine learning process is not new. The problem, however, is that we must provide the right tools to the right individuals. While fully automated, GUI-driven solutions are ideal for anyone not very familiar with the data science process; data scientists prefer to work within coding environments they love.  For any AutoML solution to provide the right degree of automation for both citizen data scientists as well as for data scientists, the automation tools must be available in forms that can be easily deployed in the development process of each.

 

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